Logo image
Feature extraction by identification of a parameterized system model
Journal article   Peer reviewed

Feature extraction by identification of a parameterized system model

J Fehlauer and B Eisenstein
IEEE transactions on automatic control, v 26(2), pp 577-580
Apr 1981

Abstract

Asymptotic stability Computational modeling Feature extraction Pattern recognition Predictive models Signal to noise ratio Speech System identification Vectors White noise
This paper focuses on extracting features from time series for pattern recognition. System identification techniques are used to represent the signals by a parameterized system model (PSM) with the parameter vector describing the PSM becoming the feature vector. A deconvolution procedure is used to enhance pattern class discrimination. The advantages of the PSM approach is a reduction of the dimensionality of the feature space thereby simplifying the classifier design and evaluation. The PSM feature extraction technique is applied to a flaw characterization problem arising from ultrasonic nondestructive testing of materials.

Metrics

16 Record Views
1 citations in Scopus

Details

InCites Highlights

Data related to this publication, from InCites Benchmarking & Analytics tool:

Collaboration types
Industry collaboration
Domestic collaboration
Web of Science research areas
Automation & Control Systems
Engineering, Electrical & Electronic
Logo image